93264 - Introductory Statistics

Learning outcomes

The course covers the fundamental aspects of probability theory and the principles of statistical inference. Upon successful completion of this Course, students are able to perform a rigorous data analysis: i) manipulate and summarize data; ii) visualize and understand relationships inside data; iii) apply the appropriate tools of probability theory and inferential statistics to extract useful information, test hypotheses and make predictions.

Course contents

1. Introduction to data: Data basics; Sampling principles; Experiments and observational studies
2. Summarizing data: Examining numerical data; Considering categorical data
3. Probability: Defining probability; Conditional probability; Bayes theorem
4. Random variables: Discrete and continuous; Expectation; Linear combination; Central limit theorem
5. Distributions of random variables: Normal; Geometric; Binomial
6. Foundations for inference: Point estimates and sampling variability; Confidence intervals; Hypothesis testing
7. Inference for numerical data: One-sample means; Paired data; Difference of two means
8. Inference for one and two proportions
   

Readings/Bibliography

David M Diez, Christopher D Barr, Mine C ̧etinkaya-Rundel (2015). OpenIntro Statistics (Fourth Edition), from Chapter 1 to Chapter 8.

This textbook is available under a Creative Commons license. Visit openintro.org for a free PDF

Teaching methods

Lectures and practical sessions in the lab

Assessment methods

One-and-a-half-hour written exam administered via the Unibo Moodle platform. The exam consists of a combination of rapid-fire questions (binary-choice and multiple-choice) and problem-solving exercises requiring numerical solutions.

For the problem-solving exercises, students are required not only to provide the final numerical answer but also to submit a handwritten solution showing the complete reasoning and all intermediate steps. This handwritten work will be assessed together with the answers submitted on Moodle.

When grading, technically correct solutions are valued along with clearly stated explanations and sound reasoning. Numerical answers alone, without adequate justification, are not sufficient to receive full credit.

Regarding the verification of learning, the use of AI is prohibited. Any use constitutes a violation of academic integrity.

Teaching tools

Tools, platforms, and resources used during the course

• Learning platform: Virtuale (virtuale.unibo.it), hosting the lecture slides and homework assignments.
• Course material: Lecture slides in PDF format.
• Communication tools: E-mail, Microsoft Teams, and the discussion forum on Virtuale.

Learning support resources

Practice quizzes

Rapid-fire quizzes are available on Virtuale. All questions are drawn from past examination papers to provide examples of the types of questions that may appear in the exam. Students are allowed up to five attempts for each quiz.

Mock exam

An important skill developed during the course is effective time management. The examination is designed so that well-prepared students can complete it comfortably within the allotted time. Nevertheless, conscious time management is essential for success and helps reduce exam-related stress. A mock exam is available on Virtuale to help students develop and refine their own time management strategy under realistic exam conditions.

Office hours

See the website of Monica Chiogna